Image Quality Assessment: Learning to Rank Image Distortion Level
- URL: http://arxiv.org/abs/2208.03317v1
- Date: Thu, 4 Aug 2022 18:33:33 GMT
- Title: Image Quality Assessment: Learning to Rank Image Distortion Level
- Authors: Shira Faigenbaum-Golovin, Or Shimshi
- Abstract summary: We learn to compare the image quality of two registered images, with respect to a chosen distortion.
Our method takes advantage of the fact that at times, simulating image distortion and later evaluating its relative image quality, is easier than assessing its absolute value.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Over the years, various algorithms were developed, attempting to imitate the
Human Visual System (HVS), and evaluate the perceptual image quality. However,
for certain image distortions, the functionality of the HVS continues to be an
enigma, and echoing its behavior remains a challenge (especially for
ill-defined distortions). In this paper, we learn to compare the image quality
of two registered images, with respect to a chosen distortion. Our method takes
advantage of the fact that at times, simulating image distortion and later
evaluating its relative image quality, is easier than assessing its absolute
value. Thus, given a pair of images, we look for an optimal dimensional
reduction function that will map each image to a numerical score, so that the
scores will reflect the image quality relation (i.e., a less distorted image
will receive a lower score). We look for an optimal dimensional reduction
mapping in the form of a Deep Neural Network which minimizes the violation of
image quality order. Subsequently, we extend the method to order a set of
images by utilizing the predicted level of the chosen distortion. We
demonstrate the validity of our method on Latent Chromatic Aberration and Moire
distortions, on synthetic and real datasets.
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